class_docstr: Class to manage a scheduled (framework-dependent) tensor variable.
  
  Uses the PiecewiseSchedule (for maximum configuration flexibility)
docstr: Updates the underlying (framework specific) tensor variable.
  
  In case of a fixed value, this method does nothing and only returns the fixed
  value as-is.
  
  Args:
      timestep: The current timestep that the update might depend on.
  
  Returns:
      The current value of the tensor variable as a python float.
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code: def update(self, timestep: int) -> float:
      """Updates the underlying (framework specific) tensor variable.
  
      In case of a fixed value, this method does nothing and only returns the fixed
      value as-is.
  
      Args:
          timestep: The current timestep that the update might depend on.
  
      Returns:
          The current value of the tensor variable as a python float.
      """
      if self.use_schedule:
          python_value = self._schedule.value(t=timestep)
          if self.framework == "torch":
              self._curr_value.data = torch.tensor(python_value)
          else:
              self._curr_value.assign(python_value)
      else:
          python_value = self._curr_value
  
      return python_value
  
file_path: ray/rllib/utils/schedules/scheduler.py
imports: from typing import Optional
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.utils.schedules.piecewise_schedule import PiecewiseSchedule
  from ray.rllib.utils.typing import LearningRateOrSchedule, TensorType
label_desc: Use image node_id 4 for calling the Scheduler obj's underlying member method code with example usage: obj.update(timestep) and returns: python_value